Method for controlling area of air supply hole of spillway tunnel

Abstract

The invention discloses a method for controlling the area of an air supply hole of a spillway tunnel. The method comprises the steps of collecting relevant parameters of the air supply hole and transmitting the relevant parameters to a controller, constructing a system mathematical model, using a self-adaptive PID control model by the controller, and adjusting the area of the air supply hole. The area of the air supply hole is directly adjusted through a self-adaptive PID control algorithm, and accurate dynamic control over the area of the air supply hole is achieved. Through real-time collection of air supply hole parameters, combination of fuzzy logic and model predictive control, and organic combination of a model predictive control module, an online parameter adjustment module and a performance evaluation module, an adaptive control algorithm is constructed, dynamic adaptive adjustment of PID parameters is realized, real-time performance feedback of the system is considered, control parameters can be automatically optimized, and the control efficiency is improved. Therefore, environmental changes and changes of internal characteristics of the system are coped, and efficient and accurate control of the complex dynamic system is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy project control, and particularly to a method for controlling the area of air intake holes in a flood discharge tunnel. Background Art

[0002] The air intake hole area control system of a flood discharge tunnel is an important field in water conservancy projects, aiming to effectively manage water flow and air flow to ensure the normal operation and safety of water conservancy facilities. In order to better control water flow and improve the efficiency and safety of water conservancy facilities, the air intake hole area control system of a flood discharge tunnel has emerged. In the early stage of water conservancy projects, the flow control in water conservancy projects mainly relied on manual operation or simple mechanical adjustment devices, which had problems such as inconvenient operation and slow response speed, and were not suitable for the requirements of large-scale water conservancy projects. With the development of automation technology, water conservancy projects began to introduce automated control systems. The initial automated control systems were mainly based on traditional PID control methods, and controlled water flow by adjusting valves or gates. However, this method had certain limitations in dealing with complex water flow conditions and changes, and it was difficult to achieve precise control. To solve the limitations of traditional PID control methods, water conservancy projects began to introduce intelligent control technologies, which could better adapt to complex water flow environments and improve the accuracy and response speed of control systems.

[0003] In recent years, although the related technologies of the air intake hole area control system of a flood discharge tunnel have been continuously improved, there are still many technical defects, including at least the following aspects: 1. The air intake hole system of a flood discharge tunnel may have nonlinearity and time-variation, which will affect the performance of the PID control algorithm, and corresponding measures need to be taken to overcome these problems. 2. The traditional PID control algorithm may fall into local optimal solutions and it is difficult to find the global optimal solution, and its robustness to parameter changes and system disturbances is poor. 3. If the parameters of the PID controller are set improperly or the environment changes greatly, it may cause the system to overshoot or oscillate, affecting the stability and performance of the system. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method for controlling the area of air intake holes in a flood discharge tunnel in view of various deficiencies of the prior art.

[0005] To solve the above technical problems, the technical solutions adopted by the present invention are as follows.

[0006] A method for constructing a control system model for the area of air intake holes in a flood discharge tunnel, comprising the following steps:

[0007] S1: Collect relevant parameters of the air intake hole and transmit them to the controller;

[0008] S2: Construct a system mathematical model;

[0009] S3: The controller uses an adaptive PID control model to calculate a control signal based on the received parameters;

[0010] S4: According to the calculated control signal, adjust the air intake hole area by controlling the actuator.

[0011] As a preferred technical solution of the present invention, the construction process of the system mathematical model is as follows:

[0012] A. Establish a preliminary physical model based on the partial differential equations of fluid motion

[0013]

[0014] where ρ is the density, v is the velocity field, p is the pressure, μ is the dynamic viscosity coefficient, f is the external force, denotes the gradient operator, which is used to describe the rate of change of a function in space; Δ denotes the Laplace operator;

[0015] B. Define the boundary conditions and initial conditions: According to the specific structure and environment of the air intake hole, set the boundary conditions and give the initial state of the flow field including the initial velocity field and pressure distribution at time t = 0;

[0016] C. Apply numerical methods to discretize and solve the equations;

[0017] D. Analyze the stability and dynamic behavior of the system in combination with the nonlinear dynamics theory;

[0018] E. Collect experimental data, use a deep learning model to learn the air flow dynamics, and optimize the parameters of the physical model;

[0019] F. Through an iterative algorithm, feedback the output of the machine learning model into the physical model to achieve more accurate prediction and control.

[0020] As a preferred technical solution of the present invention, the process of applying numerical methods to discretize and solve the equations in step C of the system mathematical model is as follows:

[0021] A. Select the finite volume method to discretize the equations and perform grid division on the computational domain. The solution of the equations will be carried out on these discrete cells. For complex air intake hole structures, unstructured grids need to be used to adapt to the geometry;

[0022] B. Convert the continuous partial differential equations into a set of algebraic equations. Using the finite volume method, integrate the flow rate and external force within the control volume to obtain a set of linear and nonlinear algebraic equations;

[0023] C. Define boundary conditions and initial conditions including the no-slip condition of the solid wall, the velocity and pressure conditions at the inlet, and the pressure condition at the outlet; for small-scale problems, use a direct solver, and for large-scale problems, use an iterative solver to solve the discretized equations.

[0024] As a preferred technical solution of the present invention, step D of the system mathematical model combines the bifurcation theory and chaos theory in the dynamic system theory to analyze the system behavior, specifically:

[0025] A. Identify key parameters that control the system behavior, including the Reynolds number, the intensity of the external driving force, etc.;

[0026] B. Determine the critical values of the system parameters, i.e., the bifurcation points; through local analysis of the system equations, identify the type of bifurcation according to the behavior of the system near the bifurcation point;

[0027] C. Use numerical simulation and theoretical analysis to identify the chaotic behavior of the system. Through long-term numerical simulation of the system trajectory, analyze its behavior in the phase space and identify the strange attractor; quantitatively describe the degree of chaos by calculating the Lyapunov exponent of the system. A positive Lyapunov exponent indicates that the system has the characteristic of sensitive dependence on the initial conditions;

[0028] D. Verify the predictions in the theoretical and numerical analysis through experimental data, and identify the nonlinear dynamics and chaotic characteristics from the experimental data; according to the experimental observations, adjust and optimize the parameters of the dynamic model to more accurately reflect the behavior of the actual system.

[0029] As a preferred technical solution of the present invention, in step S3, the adaptive PID control model further optimizes the control signal through a model predictive control module, an online parameter adjustment module, and a performance evaluation module.

[0030] As a preferred technical solution of the present invention, the construction process of the adaptive PID control model is as follows:

[0031] A. Set the baseline of performance indicators including the desired overshoot, steady-state error, rise time, etc.;

[0032] B. Implement the operation of the controlled system and collect system response data;

[0033] C. The model predictive control module combines the current system behavior data to optimize the future control signal, thereby predicting the optimal PID parameters;

[0034] D. The online parameter adjustment module constructs an adaptive control algorithm based on the results of step C, and adjusts the PID parameters according to the real-time performance indicators fed back by the system, so that when the system performance does not meet the predetermined target, the gain parameters will be automatically adjusted to adapt to environmental changes.

[0035] As a preferred technical solution of the present invention, an adaptive rule is designed to construct a neural network or a fuzzy logic controller with the system error E and the error change rate ΔE as inputs and the adjustment amounts of Kp, Ki, and Kd as outputs.

[0036] As a preferred technical solution of the present invention, the adaptive control algorithm is as follows:

[0037] Three fuzzy sets are set to describe the changes in the error E and the error change rate ΔE, namely negative large, zero, and positive large. Then, three fuzzy sets are defined to represent the adjustment amounts of each PID parameter: decrease, maintain, and increase;

[0038] The adjustment formula can be written as:

[0039] Kp' = Kp + ΔKp

[0040] Ki' = Ki + ΔKi

[0041] Kd' = Kd + ΔKd

[0042] Through fuzzy logic control, the adaptive rule is converted into specific PID parameter adjustment amounts. Assuming the PID parameters are Kp, Ki, and Kd, the adjustment amounts calculated according to the fuzzy rules are ΔKp, ΔKi, and ΔKd.

[0043] As a preferred technical solution of the present invention, the least squares method and reinforcement learning technology are applied to gradually adjust and optimize the parameters of the PID controller to reduce the system error; the performance evaluation module evaluates the adjusted performance and compares it with the preset performance indicators until the performance requirements are met.

[0044] As a preferred technical solution of the present invention, the established system model is utilized to apply MPC for predicting and optimizing future behaviors. MPC calculates a set of optimal control actions in each control cycle to minimize the future prediction error.

[0045] The beneficial effects of adopting the above technical solutions are as follows: By constructing a control system model for the air intake hole area of a flood discharge tunnel and adopting an adaptive PID control model, the present invention realizes the precise dynamic control of the air intake hole area. By combining the partial differential equations of fluid motion and a deep learning model, optimizing the physical model parameters, and establishing a more accurate system mathematical model, not only the basic dynamic characteristics of the fluid are considered, but also the prediction accuracy of the model is optimized in a data-driven manner. Through the organic combination of a model predictive control module, an online parameter adjustment module, and a performance evaluation module, an adaptive control algorithm is constructed to realize the dynamic adaptive adjustment of PID parameters. It not only considers the real-time performance feedback of the system but also can automatically optimize the control parameters to cope with environmental changes and changes in the internal characteristics of the system, achieving the efficient and precise control of a complex dynamic system.

[0046] The following embodiments detail the technical advantages and beneficial effects of the technical details of the present invention. Detailed implementation manners

[0047] The following embodiments illustrate the present invention in detail. In the description of the following embodiments, specific details such as specific system structures and technologies are presented for the purpose of illustration rather than limitation, so as to thoroughly understand the embodiments of the present application. However, those skilled in the art should understand that the present application can also be implemented in other embodiments without these specific details. In other cases, the detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid unnecessary details from interfering with the description of the present application.

[0048] It should be understood that when used in the specification and appended claims of the present application, the term "comprising" indicates the presence of the described features, wholes, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations. It should also be understood that the term "and / or" used in the specification and appended claims of the present application refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0049] As used in the specification and appended claims of the present application, the term "if" can be interpreted as "when", "once", "in response to determining", or "in response to detecting" according to the context. Similarly, the phrase "if determined" or "if [the described condition or event] is detected" can be interpreted as meaning "once determined", "in response to determining", "once [the described condition or event] is detected", or "in response to detecting [the described condition or event]" according to the context. Additionally, in the description of the specification and appended claims of the present application, the terms "first", "second", "third", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0050] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that a specific feature, structure, or characteristic described in connection with that embodiment is included in one or more embodiments of this application. Thus, statements such as "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments", etc. that appear in different places in this specification do not necessarily all refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized. The terms "comprising", "including", "having", and their variants all mean "including but not limited to", unless otherwise specifically emphasized.

[0051] Embodiment 1

[0052] A method for constructing a model of the air - supplementing hole area control system of a flood - discharge tunnel, comprising the steps of: collecting relevant parameters of the air - supplementing hole and transmitting them to a controller, constructing a system mathematical model, the controller using an adaptive PID control model (PID control means proportional - integral - derivative control) and calculating a control signal based on the received parameters, and adjusting the area of the air - supplementing hole by controlling an actuator according to the calculated control signal.

[0053] Among them, the step of collecting relevant parameters of the air - supplementing hole and transmitting them to the controller requires collecting key parameters of the operation of the air - supplementing hole, including but not limited to the temperature, pressure, flow rate of the fluid, and the size, shape, etc. of the air - supplementing hole. These parameters can be obtained by real - time measurement using sensors. The collected parameters are transmitted to the controller in real - time through a network or other communication means. To ensure the reliability and real - time nature of data transmission, industrial Ethernet or wireless communication technology can be used.

[0054] Among them, in the step of constructing the system mathematical model, first, a preliminary physical model is established based on the partial differential equation of fluid motion. This equation describes the motion state of the fluid, considering the viscous effect of the fluid and external forces. Second, according to the specific structure and operating environment of the air - supplementing hole, appropriate boundary conditions and initial conditions are defined, which include the no - slip condition of the solid wall, the pressure and velocity conditions at the inlet and outlet, etc. Third, numerical methods are applied to discretize the partial differential equation, and an appropriate mesh division is performed on the computational domain. A direct solver or an iterative solver is used to solve the discretized algebraic equations.

[0055] Among them, for the steps of using the adaptive PID control model, first, according to the control objective, performance indicators such as the desired overshoot, steady-state error, and rise time are set. Then, using the model predictive control method MPC, combined with the current system's behavior data to optimize the future control signal and predict the optimal PID parameters. Based on the results of the model predictive control, an adaptive control algorithm is constructed to adjust the PID parameters according to the real-time performance indicators to automatically adapt to environmental and system changes. Technologies such as the least squares method and reinforcement learning are applied to further adjust and optimize the parameters of the PID controller to reduce system errors. The performance evaluation module evaluates the adjusted system performance, compares it with the preset performance indicators, and adjusts the control strategy according to the results.

[0056] Among them, for the steps of the control actuator adjusting the air supplement hole area, according to the control signal calculated by the adaptive PID control model, operate the control actuator, such as an electric valve, a regulating valve, etc., to adjust the area of the air supplement hole. The actuator precisely adjusts the opening degree of the air supplement hole according to the indication of the control signal to control the amount of fluid flowing through the air supplement hole to achieve the control objective.

[0057] To further improve the adaptability and precision of the control system, a neural network or fuzzy logic controller is designed with the system error E and the error change rate ΔE as inputs and the adjustment amounts of Kp, Ki, and Kd as outputs. These controllers can intelligently adjust the PID parameters according to complex input situations to achieve more refined and flexible control.

[0058] By setting fuzzy sets to describe the changes in the error and the error change rate, and calculating the adjustment amounts of the PID parameters based on fuzzy logic, real-time adaptive adjustment of the control parameters is achieved.

[0059] Embodiment 2

[0060] Establish a data processing model for the flood discharge tunnel air supplement hole area control system to describe the dynamic relationship between the air supplement hole area, wind speed, and air supplement volume. First, assume that the gas flow in the air supplement hole can be regarded as a continuous medium, and its flow satisfies the Navier-Stokes equation. Considering the nonlinear and time-varying characteristics of the air flow in the air supplement hole, the model also needs to incorporate elements of nonlinear dynamics and appropriate handling of boundary conditions and initial conditions.

[0061] The construction process of the system mathematical model is as follows:

[0062] 1. Regard the air flow in the air supplement hole as an incompressible fluid, ignore the influence of temperature change on the flow, and establish a preliminary physical model based on the partial differential equation of fluid motion:

[0063]

[0064] where ρ is the density, v is the velocity field, p is the pressure, μ is the dynamic viscosity coefficient, and f is the external force. denotes the gradient operator, which is used to describe the rate of change of a function in space; Δ denotes the Laplace operator.

[0065] 2. Appropriately set the boundary conditions according to the specific structure and environment of the air supply hole, such as setting the boundary values of velocity and pressure at the inlet and outlet; set the initial conditions, and give the initial state of the flow field including the initial velocity field and pressure distribution at the moment of time t = 0.

[0066] 3. Apply numerical methods to discretize and solve the equations. Select the finite volume method to discretize the equations and perform grid division on the computational domain. The solution of the equations will be carried out on these discrete cells. For complex air supply hole structures, unstructured grids need to be used to adapt to the geometry; transform the continuous partial differential equations into a set of algebraic equations, and use the finite volume method to integrate the flow rate and external force within the control volume to obtain a set of linear and nonlinear algebraic equations; define the boundary conditions and initial conditions including the no-slip condition of the solid wall, the velocity and pressure conditions at the inlet, and the pressure conditions at the outlet; for small-scale problems, use a direct solver, and for large-scale problems, use an iterative solver to solve the discretized equations.

[0067] 4. Considering the possible nonlinear stability and periodic behavior of the system, combine and apply the bifurcation theory and chaos theory in the dynamic system theory to analyze the system behavior. Specifically: identify the key parameters that control the system behavior, including the Reynolds number, the intensity of the external driving force, etc.; determine the critical values of the system parameters, that is, the bifurcation points; through local analysis of the system equations, identify the type of bifurcation according to the behavior of the system near the bifurcation point; use numerical simulation and theoretical analysis to identify the chaotic behavior of the system. By performing long-term numerical simulation on the system trajectory, analyze its behavior in the phase space and identify the strange attractor; quantitatively describe the degree of chaos by calculating the Lyapunov exponent of the system. A positive Lyapunov exponent indicates that the system has the property of being sensitive to initial conditions; verify the predictions in the theory and numerical analysis through experimental data, and identify the nonlinear dynamics and chaotic characteristics from the experimental data; according to the experimental observations, adjust and optimize the parameters of the dynamic model to more accurately reflect the behavior of the actual system. Among them, the Lyapunov exponent is an index representing the local stability of the system and is a mathematical tool for describing the stability and chaotic properties of dynamic systems (such as nonlinear systems). A positive Lyapunov exponent means that small perturbations will amplify over time, indicating that the system may be in a chaotic state; a negative Lyapunov exponent indicates that the perturbations will decay over time and the system is stable; a zero Lyapunov exponent indicates that the perturbations remain unchanged and the system may be in a boundary region.

[0068] 5. Collect experimental data, use a deep learning model to learn the air flow dynamics, and optimize the parameters of the physical model; use air flow dynamic testing equipment and sensors to obtain real-time data, and collect data related to the air flow dynamics of the air replenishment hole, including the changes in parameters such as air flow velocity, temperature, and humidity over time. Based on the collected experimental data, establish a deep learning model, such as a convolutional neural network or a recurrent neural network, to learn and capture the complex patterns and features in the air flow dynamics, providing strong support for optimizing the parameters of the physical model. Train the constructed deep learning model, and through the backpropagation algorithm and optimization algorithms, continuously adjust the model parameters to minimize the error between the model prediction output and the actual observed data.

[0069] 6. Compare and analyze the output of the trained deep learning model with the prediction based on the physical model to evaluate their consistency and accuracy, and to determine the differences between the two. Based on the results of the comparative analysis, through an iterative algorithm, fuse the output of the deep learning model with the physical model to achieve more accurate prediction and control.

[0070] Example 3

[0071] The adaptive PID control model further optimizes the control signal through a model predictive control module, an online parameter adjustment module, and a performance evaluation module. First, it is necessary to clarify the desired performance indicators, such as overshoot, steady-state error, rise time, etc., which will serve as the goals for optimizing the control system. Set the baseline values of these indicators, that is, the best performance level expected to be achieved. Operate the controlled system and collect system response data, including information such as control signals, system outputs, and errors. Through these data, the actual behavior of the system can be understood, providing a basis for subsequent control optimization. Based on the collected system behavior data, use prediction methods such as physical model-based prediction and data-driven machine learning models to establish a model predictive control module. Use the prediction model to optimize future control signals, thereby predicting the optimal PID parameters.

[0072] The online parameter adjustment module constructs an adaptive control algorithm according to the results of model prediction, and adjusts the PID parameters according to the real-time performance of the system. Create a neural network or fuzzy logic controller whose input is the system error E and the error change rate ΔE, and whose output is the PID parameter adjustment amounts ΔKp, ΔKi, ΔKd. Use three fuzzy sets to describe the error E and the error change rate ΔE (negative large, zero, positive large), and three fuzzy sets to describe the adjustment amounts of the PID parameters (decrease, maintain, increase). According to the real-time error and error change rate of the system, dynamically calculate the PID parameter adjustment amounts ΔKp, ΔKi, ΔKd through fuzzy logic control or neural network to adapt to the changes in system performance.

[0073] The adjustment formula can be written as:

[0074] Kp' = Kp + ΔKp

[0075] Ki' = Ki + ΔKi

[0076] Kd' = Kd + ΔKd.

[0077] The performance evaluation module is used to monitor and evaluate the performance of the control system, collect system feedback data in real time, and calculate the actual performance index values. Compare the actual performance index values with the set baseline to evaluate whether the system reaches the expected performance level. If the system performance does not meet the predetermined goal, the online parameter adjustment module will automatically adjust the PID parameters to adapt to environmental changes and then optimize the control signal.

[0078] In various embodiments, the hardware implementation of the technology can directly adopt existing intelligent devices, including but not limited to industrial control computers, PCs, smart phones, handheld single devices, floor-standing single devices, etc. Its input device preferably adopts a screen keyboard, its data storage and calculation module adopts existing memories, calculators, and controllers, its internal communication module adopts existing communication ports and protocols, and its remote communication adopts existing GPRS networks, the World Wide Web, etc. Those skilled in the art can clearly understand that for the convenience and conciseness of description, only the above-mentioned division of each functional unit and module is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of a software functional unit. In addition, the specific names of each functional unit and module are only for the convenience of mutual distinction and do not limit the protection scope of this application. The specific working process of the units and modules in the above system can refer to the corresponding process in the foregoing method embodiments and will not be elaborated here. In the embodiments provided by the present invention, it should be understood that the disclosed device / terminal device and method can be implemented in other ways. For example, the device / terminal device embodiments described above are only illustrative. For example, the division of modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some interfaces. The indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms. The unit described as a separated component can be or can not be physically separated, and the component displayed as a unit can be or can not be a physical unit, that is, it can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Each functional unit in the various embodiments of the present invention can be integrated into a processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit.

[0079] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for controlling the area of the air intake tunnel of a flood discharge tunnel, characterized in that, It includes the following steps: S1: Obtain the air flow parameters of the air supplement hole in real time, including flow velocity, pressure and geometric parameters of the air supplement hole; S2: Input the parameters into an adaptive PID controller, and dynamically adjust the PID parameters through fuzzy logic rules; S3: Generate a control signal according to the adjusted PID parameters, and drive the actuator to adjust the opening degree of the air supplement hole.

2. The control method for the air intake area of the flood discharge tunnel according to claim 1, characterized in that The specific process of dynamically adjusting the PID parameters in step S2 includes: Set the fuzzy sets of the error E and the error change rate ΔE as {negative large, zero, positive large}; Set the fuzzy sets of the PID parameter adjustment amounts ΔKp, ΔKi, and ΔKd as {decrease, maintain, increase}; Calculate the real-time adjustment amount according to the fuzzy rule table, and update the parameters according to the following formula:

3. A method for controlling the area of the air intake tunnel of a flood discharge tunnel according to claim 2, characterized in that, The construction of the fuzzy rule table includes: When the error E is negative large and the error change rate ΔE is negative large, the output ΔKp is increase, ΔKi is maintain, and ΔKd is decrease; When the error E is zero and the error change rate ΔE is positive large, the output ΔKp is decrease, ΔKi is increase, and ΔKd is maintain; The remaining rules are preset according to the system response characteristics.

4. The control method for the air intake tunnel area of a flood discharge tunnel according to claim 1, characterized in that, Step S2 also includes: Predict the system behavior in the future time period through the model predictive control module to optimize the initial adjustment amount of the PID parameters; Combine the actual control effect feedback by the performance evaluation module to online correct the weights of the fuzzy rules.

5. The control method for the area of the air intake tunnel of a flood discharge tunnel according to claim 4, characterized in that The evaluation indexes of the performance evaluation module include overshoot, steady-state error and rise time. When any index exceeds the preset threshold, the reinforcement learning algorithm is triggered to retrain the fuzzy rule table.

6. A method for controlling the area of the air intake tunnel of a flood discharge tunnel according to any one of claims 1-5, characterized in that In step S3, the actuator is an electric control valve, and the relationship between the opening change amount and the control signal is determined by the following formula: Where, A(t) is the area of the air supplement hole at time t, A0 is the initial area, K is the proportionality coefficient, and u(τ) is the control signal.

7. A method for controlling the area of the air intake tunnel of a flood discharge tunnel according to claim 6, characterized in that, It also includes: Monitor the air flow stability in the air supplement hole in real time during the adjustment process; When chaotic characteristics are detected, dynamically limit the adjustment range of the PID parameters through the Lyapunov exponent calculation module.

8. A control system for the area of the air supply hole of a flood discharge tunnel, characterized in that, For implementing the control method according to any one of claims 1-7, it includes: A sensor module for collecting air flow parameters in real time; A fuzzy adaptive PID controller with the adjustment logic as described in claims 2-5 built in; An actuator that responds to the control signal to adjust the opening degree of the air supplement hole.

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